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---
library_name: hivex
original_train_name: AerialWildfireSuppression_difficulty_8_task_6_run_id_1_train
tags:
- hivex
- hivex-aerial-wildfire-suppression
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-AWS-PPO-baseline-task-6-difficulty-8
results:
- task:
type: sub-task
name: drop_water
task-id: 6
difficulty-id: 8
dataset:
name: hivex-aerial-wildfire-suppression
type: hivex-aerial-wildfire-suppression
metrics:
- type: crash_count
value: 0.010958191571990027 +/- 0.004573914836666436
name: Crash Count
verified: true
- type: extinguishing_trees
value: 0.18876909412210807 +/- 0.14536552612874615
name: Extinguishing Trees
verified: true
- type: extinguishing_trees_reward
value: 0.9438454601913691 +/- 0.7268276219188166
name: Extinguishing Trees Reward
verified: true
- type: preparing_trees
value: 295.6805847167969 +/- 8.689367684892192
name: Preparing Trees
verified: true
- type: preparing_trees_reward
value: 295.6805847167969 +/- 8.689367684892192
name: Preparing Trees Reward
verified: true
- type: water_drop
value: 0.9887655645608902 +/- 0.004140661152640941
name: Water Drop
verified: true
- type: water_pickup
value: 0.0006430462468415499 +/- 0.0013853557027389734
name: Water Pickup
verified: true
- type: cumulative_reward
value: 295.6266799926758 +/- 9.439199947060839
name: Cumulative Reward
verified: true
---
This model serves as the baseline for the **Aerial Wildfire Suppression** environment, trained and tested on task <code>6</code> with difficulty <code>8</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>
Environment: **Aerial Wildfire Suppression**<br>
Task: <code>6</code><br>
Difficulty: <code>8</code><br>
Algorithm: <code>PPO</code><br>
Episode Length: <code>3000</code><br>
Training <code>max_steps</code>: <code>1800000</code><br>
Testing <code>max_steps</code>: <code>180000</code><br><br>
Train & Test [Scripts](https://github.com/hivex-research/hivex)<br>
Download the [Environment](https://github.com/hivex-research/hivex-environments) |